Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "244" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 19 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 19 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.835364 | -1.123079 | -0.379642 | -1.390725 | -0.729349 | -0.514624 | 1.721171 | 4.192055 | 0.5675 | 0.6101 | 0.3986 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.181612 | -1.272052 | -0.900302 | -1.233858 | -0.900108 | -0.403846 | 2.733053 | 5.540825 | 0.5602 | 0.6072 | 0.3895 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.201838 | -1.191698 | -0.792163 | -1.100212 | -0.862071 | -0.578922 | 1.703700 | 5.452429 | 0.5517 | 0.5997 | 0.3860 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.169393 | -1.287156 | -0.867859 | -0.842097 | 0.016104 | -0.930368 | 1.978528 | 5.158827 | 0.5462 | 0.5905 | 0.3961 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.566454 | -0.795183 | -0.866255 | -0.742652 | -0.303154 | -0.413397 | 1.671032 | 4.940923 | 0.5465 | 0.5949 | 0.3927 | nan | nan |
| 2459989 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 2.243184 | -0.706861 | -0.792826 | -0.723602 | -0.350562 | -0.579791 | 1.570773 | 3.833148 | 0.5453 | 0.5973 | 0.3954 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.733908 | -0.943305 | -0.983426 | -0.803152 | 0.186732 | -0.105137 | 1.264281 | 4.339584 | 0.5477 | 0.5994 | 0.3893 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.949388 | -1.048534 | -0.850940 | -1.041502 | -0.389847 | -0.766672 | 2.503357 | 7.402845 | 0.5539 | 0.6024 | 0.3830 | nan | nan |
| 2459986 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 2.828688 | -1.156623 | -0.909425 | -0.857540 | 0.289965 | -0.599221 | 0.694706 | 2.276353 | 0.5848 | 0.6333 | 0.3420 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.556703 | -1.354583 | -0.831789 | -1.056895 | -0.561258 | -0.268809 | 3.454778 | 9.769156 | 0.5582 | 0.6061 | 0.3911 | nan | nan |
| 2459984 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 2.506404 | -1.231579 | -0.763837 | -1.093164 | -0.655182 | -1.834456 | 0.170512 | 1.996682 | 0.5740 | 0.6207 | 0.3697 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.332346 | -0.786290 | -0.815193 | -0.712927 | -0.547768 | -0.544480 | 0.984135 | 2.716747 | 0.5816 | 0.6288 | 0.3390 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.120295 | 0.181332 | -0.430045 | -1.060953 | -0.798004 | -1.079652 | -0.265069 | -0.983019 | 0.6518 | 0.6722 | 0.2728 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.533685 | 2.010557 | 1.509768 | -0.889030 | 3.369733 | 0.999377 | 3.255281 | 7.152352 | 0.4728 | 0.5709 | 0.3747 | nan | nan |
| 2459980 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.996034 | 1.704772 | 1.282303 | -0.615990 | 2.848771 | 0.760668 | 0.399954 | 0.131353 | 0.5499 | 0.6255 | 0.3040 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.706397 | 1.881074 | 1.078850 | -0.769938 | 2.486509 | -0.527201 | 2.146836 | 5.740685 | 0.4632 | 0.5673 | 0.3776 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.830764 | 2.159395 | 1.200454 | -0.817156 | 2.684658 | 0.721310 | 2.722662 | 7.870353 | 0.4649 | 0.5659 | 0.3825 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.456312 | 1.866048 | 1.316230 | -0.599906 | 2.889108 | 0.105032 | 2.936111 | 7.396361 | 0.4325 | 0.5260 | 0.3384 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.650622 | 2.111204 | 1.331888 | -0.789617 | 2.259660 | 0.773306 | 1.992313 | 6.314057 | 0.4775 | 0.5750 | 0.3730 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 4.192055 | 1.835364 | -1.123079 | -0.379642 | -1.390725 | -0.729349 | -0.514624 | 1.721171 | 4.192055 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 5.540825 | 2.181612 | -1.272052 | -0.900302 | -1.233858 | -0.900108 | -0.403846 | 2.733053 | 5.540825 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 5.452429 | 2.201838 | -1.191698 | -0.792163 | -1.100212 | -0.862071 | -0.578922 | 1.703700 | 5.452429 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 5.158827 | 3.169393 | -1.287156 | -0.867859 | -0.842097 | 0.016104 | -0.930368 | 1.978528 | 5.158827 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 4.940923 | -0.795183 | 2.566454 | -0.742652 | -0.866255 | -0.413397 | -0.303154 | 4.940923 | 1.671032 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 3.833148 | -0.706861 | 2.243184 | -0.723602 | -0.792826 | -0.579791 | -0.350562 | 3.833148 | 1.570773 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 4.339584 | -0.943305 | 2.733908 | -0.803152 | -0.983426 | -0.105137 | 0.186732 | 4.339584 | 1.264281 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 7.402845 | 1.949388 | -1.048534 | -0.850940 | -1.041502 | -0.389847 | -0.766672 | 2.503357 | 7.402845 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | ee Shape | 2.828688 | -1.156623 | 2.828688 | -0.857540 | -0.909425 | -0.599221 | 0.289965 | 2.276353 | 0.694706 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 9.769156 | -1.354583 | 2.556703 | -1.056895 | -0.831789 | -0.268809 | -0.561258 | 9.769156 | 3.454778 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | ee Shape | 2.506404 | 2.506404 | -1.231579 | -0.763837 | -1.093164 | -0.655182 | -1.834456 | 0.170512 | 1.996682 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 2.716747 | -0.332346 | -0.786290 | -0.815193 | -0.712927 | -0.547768 | -0.544480 | 0.984135 | 2.716747 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | ee Shape | 1.120295 | 1.120295 | 0.181332 | -0.430045 | -1.060953 | -0.798004 | -1.079652 | -0.265069 | -0.983019 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 7.152352 | 2.010557 | 4.533685 | -0.889030 | 1.509768 | 0.999377 | 3.369733 | 7.152352 | 3.255281 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | ee Shape | 3.996034 | 1.704772 | 3.996034 | -0.615990 | 1.282303 | 0.760668 | 2.848771 | 0.131353 | 0.399954 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 5.740685 | 4.706397 | 1.881074 | 1.078850 | -0.769938 | 2.486509 | -0.527201 | 2.146836 | 5.740685 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 7.870353 | 2.159395 | 4.830764 | -0.817156 | 1.200454 | 0.721310 | 2.684658 | 7.870353 | 2.722662 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 7.396361 | 4.456312 | 1.866048 | 1.316230 | -0.599906 | 2.889108 | 0.105032 | 2.936111 | 7.396361 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 244 | N20 | RF_maintenance | nn Temporal Discontinuties | 6.314057 | 2.111204 | 4.650622 | -0.789617 | 1.331888 | 0.773306 | 2.259660 | 6.314057 | 1.992313 |